Will It Run AI

Can Helply 10.2b chat i1 run on MacBook Pro M4 Max 128GB?

YES — Runs Great

C45Usable
Estimated from fit model

Helply 10.2b chat i1 needs ~22.1 GB VRAM. MacBook Pro M4 Max 128GB has 92.2 GB. With Q4_K_M quantization, expect ~55 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: MediumStack: StandardBottleneck: Balanced
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Operating mode

Choose the run profile you care about

Interactive favors responsiveness, while light API and scale-out lean harder on serving readiness. The fit stays the same, but the recommendation lens changes.

Current mode

Balanced

Balanced for general local use. Keeps the ranking neutral across personal and serving workflows.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 22.1 GB, 48.7 tok/s, Runs well
22.1 GB required92.2 GB available
24% VRAM used

Fit status

Runs well

Decode

48.7 tok/s

TTFT

3979 ms

Safe context

953K

Memory

22.1 GB / 92.2 GB

Memory breakdown

Weights6.2 GB
KV Cache1.2 GB
Runtime0.9 GB
Headroom13.8 GB

See how fast it feels

See how fast it feelsHelply 10.2b chat i1 on MacBook Pro M4 Max 128GB
1st promptCold start — includes initialization
>What is local AI inference?
Local AI inference means running an AI language model directly on your own hardware — your laptop, desktop, or server — instead of sending requests to a remote cloud API. When you run inference locally the model weights are loaded into your GPU or unified memory. Each token you generate requires reading those weights from memory, so memory bandwidth is the main bottleneck for decode speed. Key benefits of running locally: - Full privacy: your prompts never leave your machine - No per-token cost or rate limits - Works offline once the model is downloaded - Latency depends only on your hardware
2nd promptWarm — model ready, no init overhead
>How much VRAM do I need?
It depends on the model size and quantization level. A rough rule of thumb: Model size Q4 (4-bit) Q8 (8-bit) FP16 7B params ~4.3 GB ~7.5 GB ~14 GB 13B params ~7.9 GB ~13.9 GB ~26 GB 70B params ~42.7 GB ~74.9 GB ~140 GB Most people use 4-bit quantization (Q4_K_M) which gives 90-95% of full quality at a fraction of the memory. A 24 GB GPU can comfortably run most 7B-13B models.
Estimated: 48.7 tok/s decode · 4.0s TTFT (warm) · 122 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

Shared-memory contention still exists

The OS, browser, and inference runtime all compete for the same physical memory pool, so real-world headroom is less forgiving than raw capacity suggests.

Best improvement path

Performance by workload

WorkloadGradeFitDecodeTTFTContext
ChatCRuns well48.7 tok/s2171 ms953K
CodingCRuns well55.3 tok/s3502 ms953K
Agentic CodingCRuns well48.7 tok/s5788 ms953K
ReasoningCRuns well48.7 tok/s4703 ms953K
RAGCRuns well48.7 tok/s7235 ms953K

Quantization options

How Helply 10.2b chat i1 (10.199999809265137B params) fits at each quantization level on MacBook Pro M4 Max 128GB (92.2 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
4.0 GB
LowD39
Q3_K_S
3
5.0 GB
LowD39
NVFP4
4
5.7 GB
MediumD39
Q4_K_M
4
6.2 GB
MediumD39
Q5_K_M
5
7.3 GB
HighD39
Q6_K
6
8.4 GB
HighD39
Q8_0
8
10.9 GB
Very HighD39
F16Best for your GPU
16
20.9 GB
MaximumC40

Get started

Copy-paste commands to run Helply 10.2b chat i1 on your machine.

Run

lms load hf-mradermacher--helply-10-2b-chat-i1-gguf && lms server start

Opções de upgrade

Hardware que roda bem Helply 10.2b chat i1

Mac Studio M3 Ultra 256GBOpção econômica
256 GB Unified (+128)819 GB/s (+273)
C
Raises estimated decode speed by about 84%.89.5 tok/s decodificação

Raises estimated decode speed by about 84%.

Adds memory headroom for longer context windows and future model growth.

~$6,999 MSRP

NVIDIARTX PRO 6000 Blackwell Workstation Edition 96GBMelhor custo-benefício
1792 GB/s (+1246)
C
Raises estimated decode speed by about 193%.142.8 tok/s decodificação

Raises estimated decode speed by about 193%.

~$9,999 MSRP

Frequently asked questions

Can MacBook Pro M4 Max 128GB run Helply 10.2b chat i1?

Yes, MacBook Pro M4 Max 128GB can run Helply 10.2b chat i1 with a C grade (Runs well). Expected decode speed: 55.3 tok/s.

How much VRAM does Helply 10.2b chat i1 need?

Helply 10.2b chat i1 (10.199999809265137B parameters) requires approximately 22.1 GB of memory with Q4_K_M quantization.

What is the best quantization for Helply 10.2b chat i1?

The recommended quantization for Helply 10.2b chat i1 is Q4_K_M, which balances quality and memory efficiency.

What speed will Helply 10.2b chat i1 run at on MacBook Pro M4 Max 128GB?

On MacBook Pro M4 Max 128GB, Helply 10.2b chat i1 achieves approximately 55.3 tokens per second decode speed with a time-to-first-token of 3502ms using Q4_K_M quantization.

Can MacBook Pro M4 Max 128GB run Helply 10.2b chat i1 for coding?

For coding workloads, Helply 10.2b chat i1 on MacBook Pro M4 Max 128GB receives a C grade with 55.3 tok/s and 953K context.

What context window can Helply 10.2b chat i1 use on MacBook Pro M4 Max 128GB?

On MacBook Pro M4 Max 128GB, Helply 10.2b chat i1 can safely use up to 953K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

Is unified memory on MacBook Pro M4 Max 128GB as fast as VRAM for Helply 10.2b chat i1?

Not always. MacBook Pro M4 Max 128GB can often fit larger models thanks to unified memory, but a discrete GPU with dedicated high-bandwidth VRAM may still decode faster once the model fits. For this combination, the important distinction is capacity versus sustained throughput.

See all results for MacBook Pro M4 Max 128GBSee all hardware for Helply 10.2b chat i1
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